Revocation and Tracing Schemes for Stateless Receivers
CRYPTO '01 Proceedings of the 21st Annual International Cryptology Conference on Advances in Cryptology
CRYPTO '93 Proceedings of the 13th Annual International Cryptology Conference on Advances in Cryptology
CRYPTO '94 Proceedings of the 14th Annual International Cryptology Conference on Advances in Cryptology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
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The practical success of broadcast encryption hinges on the ability to (1) revoke the access of compromised keys and (2) determine which keys have been compromised. In this work we focus on the latter, the so-called traitor tracingproblem. The first method utilizes a Bayesian hierarchical model to replace a crucial step in a well known tracing algorithm. Previously, this step relied on worst case bounds, which often overestimate the number of tests needed to diagnose compromised keys. The second is an adaptive tracing algorithm that selects forensic tests according to the information gaincriteria. The results of the tests refine an explicit model of our beliefs that certain keys are compromised. In choosing tests based on this criteria, we significantly reduce the number of tests, as compared to the state-of-the-art techniques, required to identify compromised keys.